j
Esse commit está contido em:
Maël
externo
BIN
Arquivo binário não exibido.
Arquivo binário não exibido.
Arquivo binário não exibido.
Arquivo binário não exibido.
Arquivo binário não exibido.
+102
-32
@@ -8,6 +8,7 @@ import pandas as pd
|
||||
import time
|
||||
import re
|
||||
import os
|
||||
from collections import Counter
|
||||
|
||||
### Flask imports
|
||||
import requests
|
||||
@@ -220,39 +221,52 @@ def get_text_info(text):
|
||||
words = tokenize.word_tokenize(text)
|
||||
common_words = FreqDist(words).most_common(100)
|
||||
|
||||
counts = Counter(words)
|
||||
|
||||
num_words = len(text.split())
|
||||
return common_words, num_words
|
||||
return common_words, num_words, counts
|
||||
|
||||
@app.route('/text_1', methods=['POST'])
|
||||
def text_1():
|
||||
|
||||
text = request.form.get('text')
|
||||
traits = ['Extraversion', 'Neuroticism', 'Agreeableness', 'Conscientiousness', 'Openness']
|
||||
probas = get_personality(text)[0].tolist()
|
||||
|
||||
print(pd.DataFrame([probas], columns=traits))
|
||||
|
||||
df_text = pd.read_csv('static/js/text.txt', sep=",")
|
||||
df_new = df_text.append(pd.DataFrame([probas], columns=traits))
|
||||
df_new.to_csv('static/js/text.txt', sep=",", index=False)
|
||||
|
||||
df_text_perso = pd.read_csv('static/js/text.txt', sep=",")
|
||||
df_text_perso = pd.DataFrame([probas], columns=traits)
|
||||
perso = {}
|
||||
perso['Extraversion'] = probas[0]
|
||||
perso['Neuroticism'] = probas[1]
|
||||
perso['Agreeableness'] = probas[2]
|
||||
perso['Conscientiousness'] = probas[3]
|
||||
perso['Openness'] = probas[4]
|
||||
|
||||
df_text_perso = pd.DataFrame.from_dict(perso, orient='index')
|
||||
df_text_perso = df_text_perso.reset_index()
|
||||
df_text_perso.columns = ['Trait', 'Value']
|
||||
|
||||
df_text_perso.to_csv('static/js/text_perso.txt', sep=',', index=False)
|
||||
|
||||
print(np.mean(df_new['Extraversion']))
|
||||
|
||||
means = {}
|
||||
means['Extraversion'] = np.mean(df_new['Extraversion'])
|
||||
means['Neuroticism'] = np.mean(df_new['Neuroticism'])
|
||||
means['Agreeableness'] = np.mean(df_new['Agreeableness'])
|
||||
means['Conscientiousness'] = np.mean(df_new['Conscientiousness'])
|
||||
means['Openness'] = np.mean(df_new['Openness'])
|
||||
print(means)
|
||||
|
||||
probas_others = [np.mean(df_new['Extraversion']), np.mean(df_new['Neuroticism']), np.mean(df_new['Agreeableness']), np.mean(df_new['Conscientiousness']), np.mean(df_new['Openness'])]
|
||||
probas_others = [int(e*100) for e in probas_others]
|
||||
|
||||
df_mean = pd.DataFrame.from_dict(means, orient='index')
|
||||
df_mean = df_mean.reset_index()
|
||||
df_mean.columns = ['Trait', 'Value']
|
||||
print(df_mean)
|
||||
df_mean.to_csv('static/js/text_mean.txt', sep=',', index=False)
|
||||
|
||||
df_mean.to_csv('static/js/text_mean.txt', sep=',', index=False)
|
||||
trait_others = df_mean.ix[df_mean['Value'].idxmax()]['Trait']
|
||||
|
||||
probas = [int(e*100) for e in probas]
|
||||
|
||||
data_traits = zip(traits, probas)
|
||||
@@ -262,14 +276,34 @@ def text_1():
|
||||
session['text_info']["common_words"] = []
|
||||
session['text_info']["num_words"] = []
|
||||
|
||||
common_words, num_words = get_text_info(text)
|
||||
common_words, num_words, counts = get_text_info(text)
|
||||
|
||||
session['text_info']["common_words"].append(common_words)
|
||||
session['text_info']["num_words"].append(num_words)
|
||||
|
||||
trait = traits[probas.index(max(probas))]
|
||||
|
||||
return render_template('result.html', traits = data_traits, trait = trait, num_words = num_words, common_words = common_words)
|
||||
with open("static/js/words_perso.txt", "w") as d:
|
||||
d.write("WORDS,FREQ" + '\n')
|
||||
for line in counts :
|
||||
d.write(line + "," + str(counts[line]) + '\n')
|
||||
d.close()
|
||||
|
||||
with open("static/js/words_common.txt", "a") as d:
|
||||
for line in counts :
|
||||
d.write(line + "," + str(counts[line]) + '\n')
|
||||
d.close()
|
||||
|
||||
df_words_co = pd.read_csv('static/js/words_common.txt', sep=',')
|
||||
df_words_co.FREQ = df_words_co.FREQ.apply(pd.to_numeric)
|
||||
df_words_co = df_words_co.groupby('WORDS').sum().reset_index()
|
||||
df_words_co.to_csv('static/js/words_common.txt', sep=",", index=False)
|
||||
common_words_others = df_words_co.sort_values(by=['FREQ'], ascending=False)['WORDS'][:15]
|
||||
|
||||
df_words_perso = pd.read_csv('static/js/words_perso.txt', sep=',')
|
||||
common_words_perso = df_words_perso.sort_values(by=['FREQ'], ascending=False)['WORDS'][:15]
|
||||
|
||||
return render_template('result.html', traits = probas, trait = trait, trait_others = trait_others, probas_others = probas_others, num_words = num_words, common_words = common_words_perso, common_words_others=common_words_others)
|
||||
|
||||
ALLOWED_EXTENSIONS = set(['pdf'])
|
||||
|
||||
@@ -280,46 +314,82 @@ def allowed_file(filename):
|
||||
def text_pdf():
|
||||
f = request.files['file']
|
||||
f.save(secure_filename(f.filename))
|
||||
print(f)
|
||||
print(f.filename)
|
||||
|
||||
text = parser.from_file(f.filename)['content']
|
||||
print(text)
|
||||
|
||||
traits = ['Extraversion', 'Neuroticism', 'Agreeableness', 'Conscientiousness', 'Openness']
|
||||
probas = get_personality(text)[0].tolist()
|
||||
|
||||
print(pd.DataFrame([probas], columns=traits))
|
||||
|
||||
|
||||
df_text = pd.read_csv('static/js/text.txt', sep=",")
|
||||
df_new = df_text.append(pd.DataFrame([probas], columns=traits))
|
||||
df_new.to_csv('static/js/text.txt', sep=",", index=False)
|
||||
|
||||
df_text_perso = pd.read_csv('static/js/text.txt', sep=",")
|
||||
df_text_perso = pd.DataFrame([probas], columns=traits)
|
||||
|
||||
perso = {}
|
||||
perso['Extraversion'] = probas[0]
|
||||
perso['Neuroticism'] = probas[1]
|
||||
perso['Agreeableness'] = probas[2]
|
||||
perso['Conscientiousness'] = probas[3]
|
||||
perso['Openness'] = probas[4]
|
||||
|
||||
df_text_perso = pd.DataFrame.from_dict(perso, orient='index')
|
||||
df_text_perso = df_text_perso.reset_index()
|
||||
df_text_perso.columns = ['Trait', 'Value']
|
||||
|
||||
df_text_perso.to_csv('static/js/text_perso.txt', sep=',', index=False)
|
||||
|
||||
|
||||
means = {}
|
||||
means['Extraversion'] = np.mean(df_new['Extraversion'])
|
||||
means['Neuroticism'] = np.mean(df_new['Neuroticism'])
|
||||
means['Agreeableness'] = np.mean(df_new['Agreeableness'])
|
||||
means['Conscientiousness'] = np.mean(df_new['Conscientiousness'])
|
||||
means['Openness'] = np.mean(df_new['Openness'])
|
||||
|
||||
probas_others = [np.mean(df_new['Extraversion']), np.mean(df_new['Neuroticism']), np.mean(df_new['Agreeableness']), np.mean(df_new['Conscientiousness']), np.mean(df_new['Openness'])]
|
||||
probas_others = [int(e*100) for e in probas_others]
|
||||
|
||||
df_mean = pd.DataFrame.from_dict(means, orient='index')
|
||||
df_mean = df_mean.reset_index()
|
||||
df_mean.columns = ['Trait', 'Value']
|
||||
|
||||
df_mean.to_csv('static/js/text_mean.txt', sep=',', index=False)
|
||||
trait_others = df_mean.ix[df_mean['Value'].idxmax()]['Trait']
|
||||
|
||||
probas = [int(e*100) for e in probas]
|
||||
|
||||
|
||||
data_traits = zip(traits, probas)
|
||||
|
||||
|
||||
session['probas'] = probas
|
||||
session['text_info'] = {}
|
||||
session['text_info']["common_words"] = []
|
||||
session['text_info']["num_words"] = []
|
||||
|
||||
common_words, num_words = get_text_info(text)
|
||||
|
||||
|
||||
common_words, num_words, counts = get_text_info(text)
|
||||
|
||||
session['text_info']["common_words"].append(common_words)
|
||||
session['text_info']["num_words"].append(num_words)
|
||||
|
||||
|
||||
trait = traits[probas.index(max(probas))]
|
||||
os.remove(f.filename)
|
||||
return render_template('result.html', traits = data_traits, trait = trait, num_words = num_words, common_words = common_words)
|
||||
|
||||
|
||||
with open("static/js/words_perso.txt", "w") as d:
|
||||
d.write("WORDS,FREQ" + '\n')
|
||||
for line in counts :
|
||||
d.write(line + "," + str(counts[line]) + '\n')
|
||||
d.close()
|
||||
|
||||
with open("static/js/words_common.txt", "a") as d:
|
||||
for line in counts :
|
||||
d.write(line + "," + str(counts[line]) + '\n')
|
||||
d.close()
|
||||
|
||||
df_words_co = pd.read_csv('static/js/words_common.txt', sep=',', error_bad_lines=False)
|
||||
df_words_co.FREQ = df_words_co.FREQ.apply(pd.to_numeric)
|
||||
df_words_co = df_words_co.groupby('WORDS').sum().reset_index()
|
||||
df_words_co.to_csv('static/js/words_common.txt', sep=",", index=False)
|
||||
common_words_others = df_words_co.sort_values(by=['FREQ'], ascending=False)['WORDS'][:15]
|
||||
|
||||
df_words_perso = pd.read_csv('static/js/words_perso.txt', sep=',', error_bad_lines=False)
|
||||
common_words_perso = df_words_perso.sort_values(by=['FREQ'], ascending=False)['WORDS'][:15]
|
||||
|
||||
return render_template('result.html', traits = probas, trait = trait, trait_others = trait_others, probas_others = probas_others, num_words = num_words, common_words = common_words_perso, common_words_others=common_words_others)
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True)
|
||||
|
||||
@@ -212,4 +212,5 @@ class predict:
|
||||
y_pred = model.transform([X])
|
||||
|
||||
K.clear_session()
|
||||
|
||||
return y_pred
|
||||
|
||||
@@ -1,2 +1,2 @@
|
||||
EMOTIONS
|
||||
Angry
|
||||
Neutral
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
EMOTION,VALUE
|
||||
Angry,100
|
||||
Angry,0
|
||||
Disgust,0
|
||||
Fear,0
|
||||
Happy,0
|
||||
Neutral,0
|
||||
Neutral,100
|
||||
Sad,0
|
||||
Surprise,0
|
||||
|
||||
@@ -218,3 +218,6 @@ Angry
|
||||
Angry
|
||||
Angry
|
||||
Angry
|
||||
Disgust
|
||||
Neutral
|
||||
Neutral
|
||||
|
||||
@@ -1,9 +1,17 @@
|
||||
var margin = {top: 30, right: 30, bottom: 70, left: 60},
|
||||
width = 550 - margin.left - margin.right,
|
||||
height = 550 - margin.top - margin.bottom;
|
||||
// Set the dimensions and margins of the graph
|
||||
var margin = {top: 20, right: 20, bottom: 30, left: 70},
|
||||
width = 500 - margin.left - margin.right,
|
||||
height = 400 - margin.top - margin.bottom;
|
||||
|
||||
// set the ranges
|
||||
var x_other = d3.scaleBand()
|
||||
.range([0, width])
|
||||
.padding(0.1);
|
||||
var y_other = d3.scaleLinear()
|
||||
.range([height, 0]);
|
||||
|
||||
// append the svg object to the body of the page
|
||||
var svg = d3.select("#hist_density")
|
||||
var svg_other = d3.select("#hist_density")
|
||||
.append("svg")
|
||||
.attr("width", width + margin.left + margin.right)
|
||||
.attr("height", height + margin.top + margin.bottom)
|
||||
@@ -11,38 +19,39 @@ var svg = d3.select("#hist_density")
|
||||
.attr("transform",
|
||||
"translate(" + margin.left + "," + margin.top + ")");
|
||||
|
||||
// Parse the Data
|
||||
d3.csv("/static/js/text_mean.txt", function(data) {
|
||||
|
||||
// X axis
|
||||
var x = d3.scaleBand()
|
||||
.range([ 0, width ])
|
||||
.domain(data.map(function(d) { return d.Trait; }))
|
||||
.padding(0.2);
|
||||
svg.append("g")
|
||||
.attr("transform", "translate(0," + height + ")")
|
||||
.call(d3.axisBottom(x))
|
||||
.selectAll("text")
|
||||
.attr("transform", "translate(-10,0)rotate(-45)")
|
||||
.style("text-anchor", "end");
|
||||
// get the data
|
||||
d3.csv("static/js/text_mean.txt", function(error, data) {
|
||||
|
||||
// Add Y axis
|
||||
var y = d3.scaleLinear()
|
||||
.domain([0, d3.max(data, function(d) { return d.Value; })])
|
||||
.range([ height, 0]);
|
||||
if (error) throw error;
|
||||
|
||||
svg.append("g")
|
||||
.call(d3.axisLeft(y));
|
||||
// format the data
|
||||
data.forEach(function(d) {
|
||||
d.Value = +d.Value;
|
||||
});
|
||||
|
||||
// Bars
|
||||
svg.selectAll("mybar")
|
||||
.data(data)
|
||||
.enter()
|
||||
.append("rect")
|
||||
.attr("x", function(d) { return x(d.Trait); })
|
||||
.attr("y", function(d) { return y(+d.Value); })
|
||||
.attr("width", x.bandwidth())
|
||||
.attr("height", function(d) { return height - y(d.Value); })
|
||||
.attr("fill", "#69b3a2")
|
||||
// Scale the range of the data in the domains
|
||||
x_other.domain(data.map(function(d) { return d.Trait; }));
|
||||
y_other.domain([0, d3.max(data, function(d) { return d.Value; })]);
|
||||
|
||||
})
|
||||
// append the rectangles for the bar chart
|
||||
svg_other.selectAll(".bar")
|
||||
.data(data)
|
||||
.enter().append("rect")
|
||||
.attr("class", "bar")
|
||||
.attr("x", function(d) { return x_other(d.Trait); })
|
||||
.attr("width", x_other.bandwidth())
|
||||
.attr("y", function(d) { return y_other(d.Value); })
|
||||
.attr("height", function(d) { return height - y_other(d.Value); })
|
||||
.style("fill", "#69b3a2");
|
||||
|
||||
// add the x Axis
|
||||
svg_other.append("g")
|
||||
.attr("transform", "translate(0," + height + ")")
|
||||
.call(d3.axisBottom(x_other));
|
||||
|
||||
// add the y Axis
|
||||
svg_other.append("g")
|
||||
.call(d3.axisLeft(y_other));
|
||||
|
||||
});
|
||||
@@ -1,9 +1,17 @@
|
||||
var margin = {top: 30, right: 30, bottom: 70, left: 60},
|
||||
width = 550 - margin.left - margin.right,
|
||||
height = 550 - margin.top - margin.bottom;
|
||||
// Set the dimensions and margins of the graph
|
||||
var margin = {top: 20, right: 20, bottom: 30, left: 70},
|
||||
width = 500 - margin.left - margin.right,
|
||||
height = 400 - margin.top - margin.bottom;
|
||||
|
||||
// set the ranges
|
||||
var x = d3.scaleBand()
|
||||
.range([0, width])
|
||||
.padding(0.1);
|
||||
var y = d3.scaleLinear()
|
||||
.range([height, 0]);
|
||||
|
||||
// append the svg object to the body of the page
|
||||
var svg = d3.select("#hist_density_perso")
|
||||
var svg = d3.select("#histo")
|
||||
.append("svg")
|
||||
.attr("width", width + margin.left + margin.right)
|
||||
.attr("height", height + margin.top + margin.bottom)
|
||||
@@ -11,38 +19,38 @@ var svg = d3.select("#hist_density_perso")
|
||||
.attr("transform",
|
||||
"translate(" + margin.left + "," + margin.top + ")");
|
||||
|
||||
// Parse the Data
|
||||
d3.csv("/static/js/text_perso.txt", function(data) {
|
||||
// get the data
|
||||
d3.csv("static/js/text_perso.txt", function(error, data) {
|
||||
|
||||
// X axis
|
||||
var x = d3.scaleBand()
|
||||
.range([ 0, width ])
|
||||
.domain(data.map(function(d) { return d.Trait; }))
|
||||
.padding(0.2);
|
||||
svg.append("g")
|
||||
.attr("transform", "translate(0," + height + ")")
|
||||
.call(d3.axisBottom(x))
|
||||
.selectAll("text")
|
||||
.attr("transform", "translate(-10,0)rotate(-45)")
|
||||
.style("text-anchor", "end");
|
||||
if (error) throw error;
|
||||
|
||||
// Add Y axis
|
||||
var y = d3.scaleLinear()
|
||||
.domain([0, d3.max(data, function(d) { return d.Value; })])
|
||||
.range([ height, 0]);
|
||||
// format the data
|
||||
data.forEach(function(d) {
|
||||
d.Value = +d.Value;
|
||||
});
|
||||
|
||||
svg.append("g")
|
||||
.call(d3.axisLeft(y));
|
||||
// Scale the range of the data in the domains
|
||||
x.domain(data.map(function(d) { return d.Trait; }));
|
||||
y.domain([0, d3.max(data, function(d) { return d.Value; })]);
|
||||
|
||||
// Bars
|
||||
svg.selectAll("mybar")
|
||||
.data(data)
|
||||
.enter()
|
||||
.append("rect")
|
||||
// append the rectangles for the bar chart
|
||||
svg.selectAll(".bar")
|
||||
.data(data)
|
||||
.enter().append("rect")
|
||||
.attr("class", "bar")
|
||||
.attr("x", function(d) { return x(d.Trait); })
|
||||
.attr("y", function(d) { return y(+d.Value); })
|
||||
.attr("width", x.bandwidth())
|
||||
.attr("y", function(d) { return y(d.Value); })
|
||||
.attr("height", function(d) { return height - y(d.Value); })
|
||||
.attr("fill", "#69b3a2")
|
||||
.style("fill", "#b71b1b");
|
||||
|
||||
})
|
||||
// add the x Axis
|
||||
svg.append("g")
|
||||
.attr("transform", "translate(0," + height + ")")
|
||||
.call(d3.axisBottom(x));
|
||||
|
||||
// add the y Axis
|
||||
svg.append("g")
|
||||
.call(d3.axisLeft(y));
|
||||
|
||||
});
|
||||
@@ -2,7 +2,7 @@ EMOTION,VALUE
|
||||
Angry,13
|
||||
Disgust,0
|
||||
Fear,1
|
||||
Happy,44
|
||||
Sad,9
|
||||
Happy,52
|
||||
Sad,12
|
||||
Surprise,4
|
||||
Neutral,35
|
||||
Neutral,53
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
EMOTION,VALUE
|
||||
Angry,0
|
||||
Disgust,0
|
||||
Fear,0
|
||||
Happy,8
|
||||
Sad,3
|
||||
Surprise,0
|
||||
Neutral,18
|
||||
Fear,2
|
||||
Happy,12
|
||||
Sad,7
|
||||
Surprise,1
|
||||
Neutral,11
|
||||
|
||||
@@ -134,3 +134,36 @@ density
|
||||
6
|
||||
6
|
||||
6
|
||||
4
|
||||
6
|
||||
6
|
||||
6
|
||||
3
|
||||
3
|
||||
3
|
||||
3
|
||||
3
|
||||
3
|
||||
4
|
||||
5
|
||||
3
|
||||
3
|
||||
3
|
||||
3
|
||||
4
|
||||
4
|
||||
3
|
||||
6
|
||||
3
|
||||
6
|
||||
6
|
||||
6
|
||||
6
|
||||
4
|
||||
4
|
||||
2
|
||||
2
|
||||
4
|
||||
6
|
||||
6
|
||||
6
|
||||
|
||||
@@ -1,4 +1,6 @@
|
||||
density
|
||||
4
|
||||
6
|
||||
6
|
||||
6
|
||||
3
|
||||
@@ -7,24 +9,26 @@ density
|
||||
3
|
||||
3
|
||||
3
|
||||
4
|
||||
5
|
||||
3
|
||||
3
|
||||
3
|
||||
3
|
||||
4
|
||||
4
|
||||
3
|
||||
6
|
||||
6
|
||||
6
|
||||
6
|
||||
6
|
||||
6
|
||||
3
|
||||
6
|
||||
6
|
||||
6
|
||||
6
|
||||
4
|
||||
4
|
||||
2
|
||||
2
|
||||
4
|
||||
6
|
||||
6
|
||||
6
|
||||
6
|
||||
6
|
||||
6
|
||||
|
||||
@@ -51,4 +51,75 @@ Agreeableness,Conscientiousness,Extraversion,Neuroticism,Openness
|
||||
0.044551074504852295,0.023679757490754128,0.08012964576482773,0.4980302751064301,0.35360920429229736
|
||||
0.044551074504852295,0.023679757490754128,0.08012964576482773,0.4980302751064301,0.35360920429229736
|
||||
0.044551074504852295,0.023679757490754128,0.08012964576482773,0.4980302751064301,0.35360920429229736
|
||||
0.24518820643424988,0.22297443449497223,0.22819821536540985,0.010884138755500317,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
|
||||
0.08302895724773407,0.26300185918807983,0.11152470856904984,0.28485196828842163,0.2575925290584564
|
||||
0.07330887019634247,0.2784692049026489,0.10437306016683576,0.2865116894245148,0.25733718276023865
|
||||
0.07330887019634247,0.2784692049026489,0.10437306016683576,0.2865116894245148,0.25733718276023865
|
||||
0.07330887019634247,0.2784692049026489,0.10437306016683577,0.2865116894245148,0.25733718276023865
|
||||
0.07330887019634247,0.2784692049026489,0.10437306016683578,0.28651168942451477,0.25733718276023865
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
Trait,Value
|
||||
Extraversion,0.20440663509773757
|
||||
Neuroticism,0.11692198995008783
|
||||
Agreeableness,0.2106882502447884
|
||||
Conscientiousness,0.17407335683633135
|
||||
Openness,0.29390974434198075
|
||||
Extraversion,0.2130939582543027
|
||||
Neuroticism,0.06730739799358192
|
||||
Agreeableness,0.22359002681989823
|
||||
Conscientiousness,0.2041861231273581
|
||||
Openness,0.2918224697932601
|
||||
|
||||
@@ -1,2 +1,6 @@
|
||||
Extraversion,Neuroticism,Agreeableness,Conscientiousness,Openness
|
||||
0.22819821536540985,0.010884138755500317,0.24518820643424988,0.22297443449497223,0.29275497794151306
|
||||
Trait,Value
|
||||
Extraversion,0.10437306016683578
|
||||
Neuroticism,0.28651168942451477
|
||||
Agreeableness,0.07330887019634247
|
||||
Conscientiousness,0.2784692049026489
|
||||
Openness,0.25733718276023865
|
||||
|
||||
@@ -0,0 +1,293 @@
|
||||
WORDS,FREQ
|
||||
+,5
|
||||
-,5
|
||||
.,90
|
||||
/,5
|
||||
//maelfabien.fr,8
|
||||
20,5
|
||||
2016,5
|
||||
2019,5
|
||||
33,10
|
||||
40,5
|
||||
5,5
|
||||
7,14
|
||||
75013,5
|
||||
87,5
|
||||
:,18
|
||||
@,15
|
||||
A,5
|
||||
Actuarial,5
|
||||
Airbnb,1
|
||||
At,4
|
||||
B,2
|
||||
Barrault,5
|
||||
Being,5
|
||||
Big,5
|
||||
Boris,5
|
||||
C,5
|
||||
Corporate,5
|
||||
Cover_Letter_Maël_Fabien,1
|
||||
Cover_Letter_apple,4
|
||||
Data,5
|
||||
Dear,5
|
||||
During,10
|
||||
E,19
|
||||
Fabien,10
|
||||
Finance,10
|
||||
France,5
|
||||
French,4
|
||||
Hello,21
|
||||
Hi,31
|
||||
I,80
|
||||
IT,5
|
||||
Insurance,5
|
||||
L,9
|
||||
Lausanne,10
|
||||
MS,5
|
||||
Madam,5
|
||||
Master,5
|
||||
Maël,10
|
||||
My,5
|
||||
N,1
|
||||
Nikolov,5
|
||||
Nils,5
|
||||
OV,5
|
||||
P,8
|
||||
Parallel,5
|
||||
Paris,10
|
||||
ParisTech,4
|
||||
Professor,10
|
||||
Public,5
|
||||
R,11
|
||||
Science,5
|
||||
Since,5
|
||||
Sir,5
|
||||
Soguel,5
|
||||
Start,5
|
||||
Statistics,5
|
||||
Swiss,5
|
||||
Switzerland,10
|
||||
T,10
|
||||
Tech,5
|
||||
Telecom,9
|
||||
The,5
|
||||
Therefore,5
|
||||
These,5
|
||||
Vaudoise,5
|
||||
We,4
|
||||
Working,1
|
||||
You,5
|
||||
a,73
|
||||
about,5
|
||||
actuaries,5
|
||||
actuary,5
|
||||
agency,4
|
||||
allow,1
|
||||
always,5
|
||||
am,10
|
||||
among,4
|
||||
an,10
|
||||
analysis,4
|
||||
and,76
|
||||
any,5
|
||||
are,5
|
||||
as,20
|
||||
assistant,5
|
||||
at,16
|
||||
back,5
|
||||
been,10
|
||||
both,6
|
||||
building,5
|
||||
business,5
|
||||
called,5
|
||||
challenge,5
|
||||
chance,5
|
||||
charge,5
|
||||
choose,4
|
||||
chose,4
|
||||
clarifications,5
|
||||
clusters,5
|
||||
comfort,5
|
||||
communication,5
|
||||
company,10
|
||||
computer,10
|
||||
constantly,5
|
||||
contest,5
|
||||
continuously,5
|
||||
copie,1
|
||||
course,5
|
||||
creativity,5
|
||||
critically,5
|
||||
crowdfunding,5
|
||||
currently,5
|
||||
customer,5
|
||||
daily,5
|
||||
data,15
|
||||
datas,5
|
||||
dear,5
|
||||
decided,5
|
||||
deep,9
|
||||
developed,5
|
||||
development,5
|
||||
disposal,5
|
||||
do,5
|
||||
during,5
|
||||
earlier,5
|
||||
employment,4
|
||||
end-of-studies,5
|
||||
engineering,5
|
||||
engineers,5
|
||||
entrepreneurial,5
|
||||
entrepreneurship,5
|
||||
exam,5
|
||||
exceed,1
|
||||
experts,5
|
||||
extraction,5
|
||||
field,5
|
||||
final,5
|
||||
focusing,5
|
||||
for,39
|
||||
forward,5
|
||||
from,15
|
||||
further,5
|
||||
get,5
|
||||
given,5
|
||||
gmail.com,15
|
||||
goals,1
|
||||
going,5
|
||||
grade,5
|
||||
graduated,5
|
||||
group,4
|
||||
had,9
|
||||
handling,5
|
||||
have,35
|
||||
hearing,5
|
||||
hello,1
|
||||
help,5
|
||||
http,8
|
||||
in,64
|
||||
including,10
|
||||
insurance,5
|
||||
interest,9
|
||||
interests,5
|
||||
intern,5
|
||||
interns,5
|
||||
internship,15
|
||||
is,9
|
||||
it,5
|
||||
join,5
|
||||
jury,5
|
||||
lawyers,5
|
||||
learning,14
|
||||
limits,1
|
||||
look,10
|
||||
machine,5
|
||||
mael.fabien,15
|
||||
mailto,10
|
||||
main,5
|
||||
management,5
|
||||
marketing,5
|
||||
maximal,5
|
||||
me,2
|
||||
missing,5
|
||||
months,14
|
||||
motivates,1
|
||||
multimodal,4
|
||||
my,31
|
||||
name,5
|
||||
new,1
|
||||
non-life,5
|
||||
of,41
|
||||
offered,5
|
||||
often,5
|
||||
on,15
|
||||
one,10
|
||||
opportunities,5
|
||||
opportunity,5
|
||||
other,10
|
||||
out,5
|
||||
passionate,5
|
||||
past,10
|
||||
people,5
|
||||
personal,1
|
||||
perspective,5
|
||||
plan,1
|
||||
platform,5
|
||||
position,5
|
||||
possible,5
|
||||
post-degree,5
|
||||
present,5
|
||||
pricing,10
|
||||
prize,5
|
||||
problem,5
|
||||
product,5
|
||||
professional,1
|
||||
program,5
|
||||
project,8
|
||||
projects,9
|
||||
proposed,4
|
||||
quantitative,5
|
||||
related,5
|
||||
remain,5
|
||||
review,10
|
||||
rue,5
|
||||
s,10
|
||||
scenario,5
|
||||
school,5
|
||||
science,15
|
||||
scoring,5
|
||||
seeking,5
|
||||
sense,1
|
||||
sentiment,4
|
||||
set,1
|
||||
sets,5
|
||||
several,5
|
||||
since,9
|
||||
six,10
|
||||
skills,5
|
||||
solving,5
|
||||
specialized,5
|
||||
starting,5
|
||||
step,5
|
||||
strong,10
|
||||
students,10
|
||||
studied,5
|
||||
studies,10
|
||||
summer,5
|
||||
talented,5
|
||||
tasks,5
|
||||
teaching,5
|
||||
team,6
|
||||
teams,1
|
||||
techniques,5
|
||||
the,61
|
||||
then,5
|
||||
think,5
|
||||
this,15
|
||||
through,5
|
||||
throughout,5
|
||||
time,5
|
||||
to,60
|
||||
top,5
|
||||
try,5
|
||||
trying,5
|
||||
two,10
|
||||
undergraduate,5
|
||||
understand,5
|
||||
values,5
|
||||
was,20
|
||||
which,10
|
||||
with,6
|
||||
won,5
|
||||
work,15
|
||||
worked,15
|
||||
working,6
|
||||
would,1
|
||||
year,5
|
||||
years,10
|
||||
you,5
|
||||
your,5
|
||||
zone,5
|
||||
«,5
|
||||
»,5
|
||||
’,10
|
||||
@@ -0,0 +1,270 @@
|
||||
WORDS,FREQ
|
||||
Cover_Letter_apple,1
|
||||
Dear,1
|
||||
Madam,1
|
||||
,,32
|
||||
dear,1
|
||||
Sir,1
|
||||
My,1
|
||||
name,1
|
||||
is,2
|
||||
Maël,2
|
||||
Fabien,2
|
||||
.,18
|
||||
I,16
|
||||
have,7
|
||||
studied,1
|
||||
in,13
|
||||
Lausanne,2
|
||||
Switzerland,2
|
||||
for,8
|
||||
the,12
|
||||
past,2
|
||||
5,1
|
||||
years,2
|
||||
and,15
|
||||
graduated,1
|
||||
earlier,1
|
||||
this,3
|
||||
year,1
|
||||
from,3
|
||||
a,15
|
||||
Master,1
|
||||
Actuarial,1
|
||||
Science,1
|
||||
Statistics,1
|
||||
computer,2
|
||||
science,3
|
||||
always,1
|
||||
been,2
|
||||
my,6
|
||||
main,1
|
||||
interests,1
|
||||
throughout,1
|
||||
studies,2
|
||||
Therefore,1
|
||||
decided,1
|
||||
to,12
|
||||
join,1
|
||||
one,2
|
||||
of,8
|
||||
France,1
|
||||
’,2
|
||||
s,2
|
||||
top,1
|
||||
engineering,1
|
||||
school,1
|
||||
Telecom,2
|
||||
Paris,2
|
||||
Tech,1
|
||||
an,2
|
||||
MS,1
|
||||
Big,1
|
||||
Data,1
|
||||
post-degree,1
|
||||
program,1
|
||||
focusing,1
|
||||
on,3
|
||||
both,1
|
||||
quantitative,1
|
||||
techniques,1
|
||||
machine,1
|
||||
learning,3
|
||||
/,1
|
||||
deep,2
|
||||
am,2
|
||||
currently,1
|
||||
seeking,1
|
||||
six,2
|
||||
months,3
|
||||
internship,3
|
||||
position,1
|
||||
starting,1
|
||||
summer,1
|
||||
2019,1
|
||||
field,1
|
||||
data,3
|
||||
Parallel,1
|
||||
worked,3
|
||||
two,2
|
||||
as,4
|
||||
teaching,1
|
||||
assistant,1
|
||||
Professor,2
|
||||
Boris,1
|
||||
Nikolov,1
|
||||
Corporate,1
|
||||
Finance,2
|
||||
Nils,1
|
||||
Soguel,1
|
||||
Public,1
|
||||
had,2
|
||||
opportunity,1
|
||||
help,1
|
||||
undergraduate,1
|
||||
students,2
|
||||
problem,1
|
||||
sets,1
|
||||
solving,1
|
||||
other,2
|
||||
course,1
|
||||
related,1
|
||||
tasks,1
|
||||
These,1
|
||||
opportunities,1
|
||||
are,1
|
||||
offered,1
|
||||
scoring,1
|
||||
maximal,1
|
||||
grade,1
|
||||
at,3
|
||||
exam,1
|
||||
Swiss,1
|
||||
insurance,1
|
||||
company,2
|
||||
called,1
|
||||
«,1
|
||||
Vaudoise,1
|
||||
Insurance,1
|
||||
»,1
|
||||
During,2
|
||||
end-of-studies,1
|
||||
non-life,1
|
||||
actuary,1
|
||||
intern,1
|
||||
was,4
|
||||
charge,1
|
||||
product,1
|
||||
review,2
|
||||
including,2
|
||||
extraction,1
|
||||
handling,1
|
||||
missing,1
|
||||
values,1
|
||||
building,1
|
||||
customer,1
|
||||
clusters,1
|
||||
going,1
|
||||
through,1
|
||||
pricing,2
|
||||
scenario,1
|
||||
development,1
|
||||
The,1
|
||||
final,1
|
||||
step,1
|
||||
present,1
|
||||
management,1
|
||||
time,1
|
||||
given,1
|
||||
chance,1
|
||||
work,3
|
||||
daily,1
|
||||
with,1
|
||||
team,1
|
||||
talented,1
|
||||
people,1
|
||||
actuaries,1
|
||||
IT,1
|
||||
engineers,1
|
||||
lawyers,1
|
||||
marketing,1
|
||||
experts,1
|
||||
interns,1
|
||||
Being,1
|
||||
passionate,1
|
||||
about,1
|
||||
continuously,1
|
||||
trying,1
|
||||
think,1
|
||||
critically,1
|
||||
challenge,1
|
||||
understand,1
|
||||
datas,1
|
||||
look,2
|
||||
it,1
|
||||
business,1
|
||||
perspective,1
|
||||
constantly,1
|
||||
creativity,1
|
||||
communication,1
|
||||
skills,1
|
||||
try,1
|
||||
get,1
|
||||
out,1
|
||||
comfort,1
|
||||
zone,1
|
||||
often,1
|
||||
possible,1
|
||||
developed,1
|
||||
strong,2
|
||||
interest,2
|
||||
entrepreneurship,1
|
||||
since,2
|
||||
entrepreneurial,1
|
||||
contest,1
|
||||
Start,1
|
||||
during,1
|
||||
which,2
|
||||
won,1
|
||||
jury,1
|
||||
prize,1
|
||||
back,1
|
||||
2016,1
|
||||
Since,1
|
||||
then,1
|
||||
working,1
|
||||
projects,2
|
||||
specialized,1
|
||||
crowdfunding,1
|
||||
platform,1
|
||||
At,1
|
||||
ParisTech,1
|
||||
7,3
|
||||
group,1
|
||||
project,2
|
||||
proposed,1
|
||||
We,1
|
||||
choose,1
|
||||
among,1
|
||||
several,1
|
||||
chose,1
|
||||
multimodal,1
|
||||
sentiment,1
|
||||
analysis,1
|
||||
French,1
|
||||
employment,1
|
||||
agency,1
|
||||
do,1
|
||||
forward,1
|
||||
hearing,1
|
||||
you,1
|
||||
remain,1
|
||||
your,1
|
||||
disposal,1
|
||||
any,1
|
||||
further,1
|
||||
clarifications,1
|
||||
http,2
|
||||
:,4
|
||||
//maelfabien.fr,2
|
||||
rue,1
|
||||
Barrault,1
|
||||
75013,1
|
||||
+,1
|
||||
33,2
|
||||
87,1
|
||||
20,1
|
||||
40,1
|
||||
mael.fabien,3
|
||||
@,3
|
||||
gmail.com,3
|
||||
A,1
|
||||
P,2
|
||||
L,2
|
||||
E,4
|
||||
-,1
|
||||
C,1
|
||||
OV,1
|
||||
R,2
|
||||
T,2
|
||||
mailto,2
|
||||
Arquivo binário não exibido.
|
Antes Largura: | Altura: | Tamanho: 370 KiB Depois Largura: | Altura: | Tamanho: 213 KiB |
@@ -20,62 +20,107 @@
|
||||
|
||||
{% block body %}
|
||||
|
||||
<h4>Dominant trait : </h4>
|
||||
<h1> {{trait}} </h1>
|
||||
|
||||
<br>
|
||||
<label>Text length : {{num_words}}</label>
|
||||
<br>
|
||||
<br>
|
||||
<br>
|
||||
|
||||
<div class="row">
|
||||
<div class="column" id="left-col" align="center">
|
||||
<h2><i>Perceived Psychological Traits</i></h2>
|
||||
|
||||
<br>
|
||||
<br>
|
||||
<div class="column_home" id="left-col" align="center">
|
||||
<div>
|
||||
<div class="legend1">
|
||||
<br>
|
||||
<div class="legend1"> <p class="country-name"><span class="key-dot you"></span>Your personal psychological traits perceived</p> </div>
|
||||
</div>
|
||||
<div id="hist_density_perso"></div>
|
||||
</div>
|
||||
<div class="column" id="left-col" align="center">
|
||||
|
||||
<div class="column" id="right-col" align="center">
|
||||
|
||||
<div class="legend1">
|
||||
<br>
|
||||
<div class="legend1"> <p class="country-name"><span class="key-dot others"></span>Other candidates' personal psychological traits perceived</p> </div>
|
||||
</div>
|
||||
<div id="hist_density"></div>
|
||||
</div>
|
||||
<div class="column" id="right-col" align="center">
|
||||
|
||||
<div class="parent">
|
||||
<p>Probabilities of each trait: </p>
|
||||
<ul align="left">
|
||||
{% for name, proba in traits%}
|
||||
<li>{{name}} : {{proba}}%</li>
|
||||
{% endfor %}
|
||||
</ul>
|
||||
<div class="legend1"> <p class="country-name"><span class="key-dot you"></span>You</p> </div>
|
||||
<div class="legend1"> <p class="country-name"><span class="key-dot others"></span>Other candidates</p> </div>
|
||||
</div>
|
||||
<br>
|
||||
<hr width="50%" style="margin-left: 25%">
|
||||
<br>
|
||||
<div class="parent">
|
||||
<p>Most common words : </p>
|
||||
<ul align="left">
|
||||
{% for el in common_words[:15] %}
|
||||
<li>{{el[0]}}</li>
|
||||
{% endfor %}
|
||||
</ul>
|
||||
<div id="histo"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="column_home" id="left-col" align="center">
|
||||
<br>
|
||||
<p>Your most visible trait is : </p>
|
||||
<br>
|
||||
<h4> {{trait}} </h4>
|
||||
<br>
|
||||
<div class="parent">
|
||||
<p>Psychological Traits : </p>
|
||||
<ul align="left">
|
||||
<li>Extraversion : {{traits[0]}}%</li>
|
||||
<li>Neuroticism : {{traits[1]}}%</li>
|
||||
<li>Agreeableness : {{traits[2]}}%</li>
|
||||
<li>Conscientiousness : {{traits[3]}}%</li>
|
||||
<li>Openness : {{traits[4]}}%</li>
|
||||
</ul>
|
||||
</div>
|
||||
<br>
|
||||
<br>
|
||||
</div>
|
||||
|
||||
<div class="column_home" id="left-col" align="center">
|
||||
<div class="parent">
|
||||
<p>Most common words : </p>
|
||||
<ul align="left">
|
||||
{% for el in common_words %}
|
||||
<li>{{el}}</li>
|
||||
{% endfor %}
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<script type="text/javascript" src="static/js/hist_txt.js"></script>
|
||||
<script type="text/javascript" src="static/js/hist_txt_perso.js"></script>
|
||||
<!--<img src='newplot.png'>-->
|
||||
<br>
|
||||
<br>
|
||||
<br>
|
||||
<br>
|
||||
<hr width="50%" style="margin-left: 25%; margin-right:25%">
|
||||
<br>
|
||||
<br>
|
||||
|
||||
|
||||
<div class="row">
|
||||
<h2><i>Other candidates</i></h2>
|
||||
|
||||
<br>
|
||||
<br>
|
||||
<div class="column_home" id="left-col" align="center">
|
||||
<div>
|
||||
<div id="hist_density"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="column_home" id="left-col" align="center">
|
||||
<br>
|
||||
<p>Their most visible trait is : </p>
|
||||
<br>
|
||||
<h4> {{trait_others}} </h4>
|
||||
<br>
|
||||
<div class="parent">
|
||||
<p>Psychological Traits : </p>
|
||||
<ul align="left">
|
||||
<li>Extraversion : {{probas_others[0]}}%</li>
|
||||
<li>Neuroticism : {{probas_others[1]}}%</li>
|
||||
<li>Agreeableness : {{probas_others[2]}}%</li>
|
||||
<li>Conscientiousness : {{probas_others[3]}}%</li>
|
||||
<li>Openness : {{probas_others[4]}}%</li>
|
||||
</ul>
|
||||
</div>
|
||||
<br>
|
||||
<br>
|
||||
</div>
|
||||
|
||||
<div class="column_home" id="left-col" align="center">
|
||||
<div class="parent">
|
||||
<p>Most common words : </p>
|
||||
<ul align="left">
|
||||
{% for el in common_words_others %}
|
||||
<li>{{el}}</li>
|
||||
{% endfor %}
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<script type="text/javascript" src="static/js/hist_txt_perso.js"></script>
|
||||
<script type="text/javascript" src="static/js/hist_txt.js"></script>
|
||||
|
||||
<form>
|
||||
<input type="button" value="Back" onclick="history.go(-1)">
|
||||
|
||||
Arquivo binário não exibido.
Referência em uma Nova Issue
Bloquear um usuário